Craft Data Science Insights: A 40-Minute Exploration – Session #5

Get ready to be inspired! This miniseries brings together five brilliant young minds from education, psychology, learning science, and survey methodology. They’ll share their innovative solutions to challenges, powered by AI and data science.

Introduction to Mixed-Mode Surveys

Dr. Tuba Suzer-Gurtekin, University of Michigan

Although data collection mode decision has always been one of the key components in survey designs, recently survey researchers face a greater complexity in data collection mode decisions. This increasing complexity is a result of technological developments and a better understanding of how mode affects measurement error in particular. In this talk, we will explore the decision on data collection mode in the context of mixed-mode surveys. Briefly, mixed-mode surveys use a combination of data collection methods to increase coverage, response rates, and data quality given available sampling frames. Mixed-mode survey design process involves dynamic survey error trade-off discussions that simultaneously rely on empirical findings, practical knowledge, and theory. As a result, there is an extra burden on the survey researcher to be aware of the specific gaps and assumptions that are made in specific designs and what the implications of these assumptions are for the survey inference. The key objective of the presentation is to introduce the audience to the components of the survey error trade-off decisions in mixed-mode surveys design and implementation. I will cover the proposed theories, the existing gaps in the literature, and case studies presented to simulate design processes in real-life settings. I will also introduce specific common designs and motivations behind these common designs, including the data analysis methods. In conclusion, I will present the basic knowledge and the understanding of practical needs, constraints that are behind various mixed-mode surveys, and theories and principles that govern the specific mixed-mode survey designs, implementation, and data analysis.

About the Presenter

Z. Tuba Suzer-Gurtekin is an Assistant Research Scientist at the University of Michigan’s Institute for Social Research. Her research includes managing monthly surveys of consumer attitudes, expectations and behavior and developing estimation methods that can improve the reliability of the low response rate probability sample surveys. Her published research focuses on methods to quantify nonresponse and measurement survey errors in probability and nonprobability sample surveys, and mixed-mode survey design and inference. Her research experience has included development of alternative sample, methodology and questionnaire designs, data collection and analysis methods for a general population in parallel survey modes. She also serves on the Board of Associate Editors of CDC’s Preventing Chronic Disease journal.

Dr. Tuba Suzer-Gurtekin
Assistant Research Scientist
Survey Research Center
Institute for Social Research
Quantitative Methods and Social Science Program
Clinical Research Design and Statistical Analysis Program
University of Michigan

Contact: Hua-Hua Chang, chang606@purdue.edu